System

Abstract

An object of a system according to an embodiment is to automatically evaluate and visually display a surrounding environment and convenience of a real estate property.SOLUTION: A system includes a peripheral facility information collection part, a convenience evaluation part, and a display part. The peripheral facility information collection unit automatically collects information on facilities, transportation facilities, natural environments, and the like existing around the real estate property. The convenience evaluation unit evaluates the convenience of the surroundings of each property on the basis of the information collected by the surrounding facility information collection unit and on the basis of the user's preferences and emphasis points. The display part visually displays a result evaluated by the convenience evaluation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, evaluating the surrounding environment and convenience of a real estate property was a time-consuming and labor-intensive process.

[0005] The system according to the embodiment aims to automatically evaluate the surrounding environment and convenience of a real estate property and visually display the evaluation results. [Means for solving the problem]

[0006] The system according to the embodiment includes a surrounding facility information collection unit, a convenience evaluation unit, and a display unit. The surrounding facility information collection unit automatically collects information on facilities, transportation, the natural environment, and the like present around a real estate property. The convenience evaluation unit evaluates the convenience of the area around each property based on the user's preferences and priorities, using the information collected by the surrounding facility information collection unit. The display unit visually displays the results of the evaluation by the convenience evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically evaluate the surrounding environment and convenience of a real estate property and visually display it. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A real estate property selection system according to an embodiment of the present invention automatically identifies information about facilities, transportation, natural environments, and other factors surrounding real estate properties and maps them on a map. It also has a function for evaluating the convenience of the surrounding area of ​​each property based on the user's preferences and priorities. This allows the real estate property selection system to significantly reduce the time and effort required for property selection, enabling users to efficiently select the most suitable property.

[0029] A real estate property selection system according to an embodiment includes a surrounding facility information collection unit, a convenience evaluation unit, and a display unit. The surrounding facility information collection unit automatically collects information about facilities, transportation, natural environments, and other factors surrounding a real estate property. For example, the surrounding facility information collection unit uses a generation AI to collect information about supermarkets, convenience stores, hospitals, schools, train stations, parks, and other facilities based on the property's address and location information, and maps the information on a map. The surrounding facility information collection unit can also obtain the latest facility information using online databases and APIs. For example, when a property address is input, the generation AI collects information about surrounding facilities and displays it on a map. The convenience evaluation unit evaluates the convenience of the surrounding area of ​​each property based on the user's preferences and priorities, using the information collected by the surrounding facility information collection unit. For example, the convenience evaluation unit scores the convenience of each property based on user-entered criteria such as "distance from the station," "proximity to a supermarket," and "presence or absence of a park." The convenience evaluation unit can also use the generation AI to analyze the user's preferences and priorities and set optimal evaluation criteria. For example, the generation AI evaluates the convenience of the surrounding area of ​​each property based on the user's input information. The display unit visually displays the results of the evaluation by the convenience evaluation unit. For example, the display unit displays the locations of facilities surrounding each property on a map, and indicates the distance and travel time to each facility. The display unit can also display an overall evaluation of each property as a score based on the user's preferences and priorities. For example, the display unit displays the evaluation results on a map, allowing the user to easily compare the surrounding environment and convenience of properties. This allows the real estate property selection system according to the embodiment to significantly reduce the effort and time required for property selection and enable the user to efficiently select the optimal property. For example, the user can easily understand the surrounding environment and convenience of properties through the system and select the optimal property. Furthermore, the system provides evaluation results based on the user's preferences and priorities, thereby improving user satisfaction.

[0030] The surrounding facility information collection unit collects facility opening hours and congestion status in real time, allowing users to optimize the timing of their visit. For example, using a generation AI, the surrounding facility information collection unit collects facility opening hours in real time and displays them on a map. For example, it displays the opening hours of supermarkets and restaurants and suggests the optimal time for users to visit. The surrounding facility information collection unit also collects congestion status in real time, allowing users to use facilities while avoiding crowds. For example, the generation AI can monitor the congestion status of facilities using sensors and cameras and suggest the optimal time to visit to users. This allows users to use facilities at the optimal time.

[0031] The surrounding facility information collection unit can collect reviews and ratings of surrounding facilities and provide users with a function to evaluate the quality of the facilities. For example, the surrounding facility information collection unit uses a generation AI to collect ratings of surrounding facilities from online review sites and display them on a map. For example, it can display ratings of restaurants and cafes to make it easier for users to make selections. The surrounding facility information collection unit can also score the quality of facilities based on reviews and ratings. For example, the generation AI can analyze the content of reviews and evaluate the cleanliness and quality of service of the facility. This makes it easier for users to evaluate the quality of the facility.

[0032] The surrounding facility information collection unit can collect information on nearby events and special seasonal activities and suggest them to the user. The surrounding facility information collection unit, for example, uses a generation AI to collect information on nearby events and display it on a map. For example, it displays information on concerts and festivals and suggests it to the user. The surrounding facility information collection unit can also collect special seasonal activities and suggest them to the user. For example, the generation AI collects information on local festivals and seasonal activities and suggests it to the user. This makes it easier for the user to understand nearby events and seasonal activities.

[0033] The surrounding facility information collection unit can provide a function to collect surrounding crime rate and public safety information and evaluate safety. For example, the surrounding facility information collection unit uses the generation AI to collect surrounding crime rate information from police and local government databases and display it on a map. For example, it can display the number of crimes and types of crimes and evaluate safety. The surrounding facility information collection unit can also collect public safety information and evaluate safety. For example, the generation AI can collect information on the frequency of police patrols and the public safety situation in the area and evaluate safety. This makes it easier for users to understand the safety of their surroundings.

[0034] The usability evaluation unit allows the generation AI to automatically estimate and suggest preferences and important points based on the user's past search history and selection history. The usability evaluation unit, for example, uses the generation AI to analyze the user's past search history and automatically estimate preferences and important points. For example, it makes suggestions based on the characteristics of properties searched for in the past. The usability evaluation unit can also suggest optimal properties based on the user's selection history. For example, the generation AI analyzes the conditions of properties selected by the user in the past and suggests similar properties. This allows optimal suggestions to be made based on the user's past behavior.

[0035] The usability evaluation unit allows the generation AI to provide customized evaluation criteria based on the user's lifestyle and family composition. The usability evaluation unit, for example, uses the generation AI to collect lifestyle information about the user and provide customized evaluation criteria. For example, for a user who loves the outdoors, the evaluation will emphasize parks and natural environments. The usability evaluation unit can also suggest the most suitable property based on the user's family composition. For example, the generation AI will consider whether the user has children and pets and suggest the most suitable property. This allows evaluations to be made according to the user's individual needs.

[0036] The convenience evaluation unit allows the generation AI to automatically suggest similar properties based on the preferences and priorities entered by the user. The convenience evaluation unit, for example, uses the generation AI to analyze the preferences and priorities entered by the user and automatically suggest similar properties. For example, it may suggest properties near a station or a supermarket. The convenience evaluation unit can also suggest optimal properties based on the user's preferences and priorities. For example, the generation AI analyzes the conditions entered by the user and suggests similar properties. This allows the user to easily find similar properties based on their preferences and priorities.

[0037] The usability evaluation unit allows the generation AI to provide surrounding community information and resident reviews based on the preferences and points of importance input by the user. The usability evaluation unit, for example, uses the generation AI to collect and provide surrounding community information based on the preferences and points of importance input by the user. For example, it displays information about local events and activities. The usability evaluation unit can also collect resident reviews and provide them to the user. For example, the generation AI collects resident satisfaction and public safety information and provides it to the user. This allows the user to refer to surrounding community information and resident reviews.

[0038] The display unit can visually display the results of the convenience evaluation using an interactive map or 3D model, allowing the user to intuitively understand the results. For example, the display unit uses a generating AI to display the results of the convenience evaluation as an interactive map, allowing the user to access detailed information by clicking or zooming. For example, detailed information about each facility is displayed. The display unit can also visually display the surrounding environment of the property using a 3D model. For example, the generating AI displays the surroundings of the property as a 3D model, allowing the user to intuitively understand the results. This allows the user to intuitively understand the results of the convenience evaluation.

[0039] The display unit can notify the user of the results of the convenience evaluation to their smartphone or wearable device, providing information in real time. The display unit can, for example, use a generation AI to notify the user of the results of the convenience evaluation to their smartphone, providing information in real time. For example, it can send information about facilities around the property and the evaluation results via push notification. The display unit can also provide the evaluation results in real time using a wearable device. For example, the generation AI can display the evaluation results on a smartwatch, allowing the user to check the information at any time. This allows the user to receive the results of the convenience evaluation in real time.

[0040] The display unit can compare the results of the convenience evaluation with the ratings and reviews of other users and provide a relative evaluation. The display unit, for example, uses a generation AI to compare the results of the convenience evaluation with the ratings and reviews of other users and provide a relative evaluation. For example, the display unit displays the evaluation results in comparison with other properties in the same area. The display unit can also provide a relative evaluation of the property based on the ratings and reviews of other users. For example, the generation AI analyzes the ratings of other users and displays the relative evaluation of the property. This allows the user to compare the ratings of other users and select a property.

[0041] The display unit can provide a function that links the results of the usability evaluation with the user's social media account and allows the results to be shared with friends and family. For example, the display unit can use the generation AI to link the results of the usability evaluation with the user's social media account and provide a function that allows the results to be shared with friends and family. For example, a function to post the evaluation results to a social media site can be added. The display unit can also share the evaluation results in real time through social media. For example, the generation AI posts the evaluation results on Facebook or Twitter to share them with friends and family. This allows the user to easily share the evaluation results with friends and family.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The real estate property selection system can further include an energy efficiency evaluation unit. The energy efficiency evaluation unit collects energy consumption data of properties and evaluates their energy efficiency. For example, the energy efficiency evaluation unit analyzes the property's electricity consumption and gas usage and scores its energy efficiency. The energy efficiency evaluation unit can also evaluate the property's insulation performance and whether or not it has a solar power generation system, and make suggestions for improving its energy efficiency. This makes it easier for users to select properties with high energy efficiency.

[0044] The real estate property selection system can further include a health assessment unit. The health assessment unit collects information on the air quality and noise levels around the property and evaluates the impact on health. For example, the health assessment unit monitors PM2.5 and CO2 concentrations around the property to evaluate air quality. The health assessment unit can also measure the noise levels around the property and suggest properties that offer a quiet environment. This makes it easier for users to select properties that are considerate of their health.

[0045] The real estate property selection system can further include an education evaluation unit. The education evaluation unit collects information on schools and educational facilities around the property and evaluates the educational environment. For example, the education evaluation unit collects academic achievement test results and advancement rates of schools around the property and scores the educational environment. The education evaluation unit can also collect information on cram schools and extracurricular activities around the property and make suggestions to improve the educational environment. This makes it easier for users to select properties suitable for their children's education.

[0046] The real estate property selection system can further include an ecological evaluation unit. The ecological evaluation unit evaluates the environmental impact of a property and suggests ecologically friendly properties. For example, the ecological evaluation unit analyzes the property's energy consumption and recycling rate to score the environmental impact. The ecological evaluation unit can also collect information on ecological facilities and nature reserves around the property and suggest ecologically friendly properties. This makes it easier for users to select ecologically friendly properties.

[0047] The real estate property selection system can further include a transportation evaluation unit. The transportation evaluation unit evaluates transportation access around the property and suggests properties with high transportation convenience. For example, the transportation evaluation unit collects information on the operation status of public transportation and traffic congestion around the property and scores transportation convenience. The transportation evaluation unit can also collect information on bicycle paths and pedestrian-only roads around the property and make suggestions to improve transportation convenience. This makes it easier for users to select properties with high transportation convenience.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The surrounding facility information collection unit automatically collects information on facilities, transportation, and the natural environment around the real estate property. For example, using generation AI, information on supermarkets, convenience stores, hospitals, schools, stations, parks, etc. is collected based on the property's address and location information, and then mapped on a map. It is also possible to obtain the latest facility information using online databases and APIs. Step 2: The convenience evaluation unit evaluates the convenience of the surrounding area of ​​each property based on the user's preferences and priorities, using the information collected by the surrounding facility information collection unit. For example, the convenience of each property is scored based on items entered by the user, such as "distance from the station," "proximity to a supermarket," and "whether or not there is a park." It is also possible to use generation AI to analyze the user's preferences and priorities and set optimal evaluation criteria. Step 3: The display unit visually displays the results of the evaluation by the convenience evaluation unit. For example, it may display the locations of facilities around each property on a map, and indicate the distance and travel time to each facility. It may also display an overall score for each property based on the user's preferences and priorities.

[0050] (Example 2) A real estate property selection system according to an embodiment of the present invention automatically identifies information about facilities, transportation, natural environments, and other factors surrounding real estate properties and maps them on a map. It also has a function for evaluating the convenience of the surrounding area of ​​each property based on the user's preferences and priorities. This allows the real estate property selection system to significantly reduce the time and effort required for property selection, enabling users to efficiently select the most suitable property.

[0051] A real estate property selection system according to an embodiment includes a surrounding facility information collection unit, a convenience evaluation unit, and a display unit. The surrounding facility information collection unit automatically collects information about facilities, transportation, natural environments, and other factors surrounding a real estate property. For example, the surrounding facility information collection unit uses a generation AI to collect information about supermarkets, convenience stores, hospitals, schools, train stations, parks, and other facilities based on the property's address and location information, and maps the information on a map. The surrounding facility information collection unit can also obtain the latest facility information using online databases and APIs. For example, when a property address is input, the generation AI collects information about surrounding facilities and displays it on a map. The convenience evaluation unit evaluates the convenience of the surrounding area of ​​each property based on the user's preferences and priorities, using the information collected by the surrounding facility information collection unit. For example, the convenience evaluation unit scores the convenience of each property based on user-entered criteria such as "distance from the station," "proximity to a supermarket," and "presence or absence of a park." The convenience evaluation unit can also use the generation AI to analyze the user's preferences and priorities and set optimal evaluation criteria. For example, the generation AI evaluates the convenience of the surrounding area of ​​each property based on the user's input information. The display unit visually displays the results of the evaluation by the convenience evaluation unit. For example, the display unit displays the locations of facilities surrounding each property on a map, and indicates the distance and travel time to each facility. The display unit can also display an overall evaluation of each property as a score based on the user's preferences and priorities. For example, the display unit displays the evaluation results on a map, allowing the user to easily compare the surrounding environment and convenience of properties. This allows the real estate property selection system according to the embodiment to significantly reduce the effort and time required for property selection and enable the user to efficiently select the optimal property. For example, the user can easily understand the surrounding environment and convenience of properties through the system and select the optimal property. Furthermore, the system provides evaluation results based on the user's preferences and priorities, thereby improving user satisfaction.

[0052] The surrounding facility information collection unit collects facility opening hours and congestion status in real time, allowing users to optimize the timing of their visit. For example, using a generation AI, the surrounding facility information collection unit collects facility opening hours in real time and displays them on a map. For example, it displays the opening hours of supermarkets and restaurants and suggests the optimal time for users to visit. The surrounding facility information collection unit also collects congestion status in real time, allowing users to use facilities while avoiding crowds. For example, the generation AI can monitor the congestion status of facilities using sensors and cameras and suggest the optimal time to visit to users. This allows users to use facilities at the optimal time.

[0053] The surrounding facility information collection unit can collect reviews and ratings of surrounding facilities and provide users with a function to evaluate the quality of the facilities. For example, the surrounding facility information collection unit uses a generation AI to collect ratings of surrounding facilities from online review sites and display them on a map. For example, it can display ratings of restaurants and cafes to make it easier for users to make selections. The surrounding facility information collection unit can also score the quality of facilities based on reviews and ratings. For example, the generation AI can analyze the content of reviews and evaluate the cleanliness and quality of service of the facility. This makes it easier for users to evaluate the quality of the facility.

[0054] The nearby facility information collection unit can use the emotion estimation function to estimate students' emotions and reflect the emotion data in the evaluation of their answers. For example, the nearby facility information collection unit can use the generation AI to analyze users' emotions from word-of-mouth and reviews, and prioritize displaying facilities with positive emotions. For example, it can highlight facilities with high levels of joy and satisfaction. The nearby facility information collection unit can also use the emotion estimation function to monitor users' emotions in real time and suggest facilities that elicit positive emotions. For example, the generation AI can analyze the user's facial expressions and voice, and prioritize displaying facilities with positive emotions. This allows the user to prioritize selecting facilities with positive emotions.

[0055] The surrounding facility information collection unit can collect information on nearby events and special seasonal activities and suggest them to the user. The surrounding facility information collection unit, for example, uses a generation AI to collect information on nearby events and display it on a map. For example, it displays information on concerts and festivals and suggests it to the user. The surrounding facility information collection unit can also collect special seasonal activities and suggest them to the user. For example, the generation AI collects information on local festivals and seasonal activities and suggests it to the user. This makes it easier for the user to understand nearby events and seasonal activities.

[0056] The surrounding facility information collection unit can provide a function to collect surrounding crime rate and public safety information and evaluate safety. For example, the surrounding facility information collection unit uses the generation AI to collect surrounding crime rate information from police and local government databases and display it on a map. For example, it can display the number of crimes and types of crimes and evaluate safety. The surrounding facility information collection unit can also collect public safety information and evaluate safety. For example, the generation AI can collect information on the frequency of police patrols and the public safety situation in the area and evaluate safety. This makes it easier for users to understand the safety of their surroundings.

[0057] The surrounding facility information collection unit can use the emotion estimation function to analyze the emotions toward the property address entered by the user and make suggestions that elicit positive emotions. The surrounding facility information collection unit can, for example, use the generation AI to analyze the emotions toward the property address entered by the user and make suggestions that elicit positive emotions. For example, areas that evoke positive emotions can be preferentially displayed. The surrounding facility information collection unit can also use the emotion estimation function to monitor the user's emotions in real time and suggest properties that evoke positive emotions. For example, the generation AI can analyze the user's facial expressions and voice and preferentially display properties that evoke positive emotions. This makes it easier for the user to select properties that evoke positive emotions.

[0058] The usability evaluation unit allows the generation AI to automatically estimate and suggest preferences and important points based on the user's past search history and selection history. The usability evaluation unit, for example, uses the generation AI to analyze the user's past search history and automatically estimate preferences and important points. For example, it makes suggestions based on the characteristics of properties searched for in the past. The usability evaluation unit can also suggest optimal properties based on the user's selection history. For example, the generation AI analyzes the conditions of properties selected by the user in the past and suggests similar properties. This allows optimal suggestions to be made based on the user's past behavior.

[0059] The usability evaluation unit allows the generation AI to provide customized evaluation criteria based on the user's lifestyle and family composition. The usability evaluation unit, for example, uses the generation AI to collect lifestyle information about the user and provide customized evaluation criteria. For example, for a user who loves the outdoors, the evaluation will emphasize parks and natural environments. The usability evaluation unit can also suggest the most suitable property based on the user's family composition. For example, the generation AI will consider whether the user has children and pets and suggest the most suitable property. This allows evaluations to be made according to the user's individual needs.

[0060] The usability evaluation unit can use the emotion estimation function to analyze the emotions regarding the preferences and important points entered by the user and make suggestions that elicit positive emotions. The usability evaluation unit can, for example, use the generation AI to analyze the emotions regarding the preferences and important points entered by the user and make positive suggestions. For example, points that evoke positive emotions can be preferentially displayed. The usability evaluation unit can also use the emotion estimation function to monitor the user's emotions in real time and suggest properties that evoke positive emotions. For example, the generation AI can analyze the user's facial expressions and voice and preferentially display properties that evoke positive emotions. This makes it easier for the user to select properties that evoke positive emotions.

[0061] The convenience evaluation unit allows the generation AI to automatically suggest similar properties based on the preferences and priorities entered by the user. The convenience evaluation unit, for example, uses the generation AI to analyze the preferences and priorities entered by the user and automatically suggest similar properties. For example, it may suggest properties near a station or a supermarket. The convenience evaluation unit can also suggest optimal properties based on the user's preferences and priorities. For example, the generation AI analyzes the conditions entered by the user and suggests similar properties. This allows the user to easily find similar properties based on their preferences and priorities.

[0062] The usability evaluation unit allows the generation AI to provide surrounding community information and resident reviews based on the preferences and points of importance input by the user. The usability evaluation unit, for example, uses the generation AI to collect and provide surrounding community information based on the preferences and points of importance input by the user. For example, it displays information about local events and activities. The usability evaluation unit can also collect resident reviews and provide them to the user. For example, the generation AI collects resident satisfaction and public safety information and provides it to the user. This allows the user to refer to surrounding community information and resident reviews.

[0063] The usability evaluation unit can use the emotion estimation function to collect other users' emotional reactions to the preferences and important points entered by the user and provide them as reference information. The usability evaluation unit can, for example, use a generation AI to collect other users' emotional reactions and provide them as reference information. For example, points associated with positive emotions can be displayed preferentially. The usability evaluation unit can also use the emotion estimation function to monitor other users' emotions in real time and provide them as reference information. For example, the generation AI can analyze other users' facial expressions and voices and display points associated with positive emotions preferentially. This allows users to refer to the emotional reactions of other users.

[0064] The display unit can visually display the results of the convenience evaluation using an interactive map or 3D model, allowing the user to intuitively understand the results. For example, the display unit uses a generating AI to display the results of the convenience evaluation as an interactive map, allowing the user to access detailed information by clicking or zooming. For example, detailed information about each facility is displayed. The display unit can also visually display the surrounding environment of the property using a 3D model. For example, the generating AI displays the surroundings of the property as a 3D model, allowing the user to intuitively understand the results. This allows the user to intuitively understand the results of the convenience evaluation.

[0065] The display unit can notify the user of the results of the convenience evaluation to their smartphone or wearable device, providing information in real time. The display unit can, for example, use a generation AI to notify the user of the results of the convenience evaluation to their smartphone, providing information in real time. For example, it can send information about facilities around the property and the evaluation results via push notification. The display unit can also provide the evaluation results in real time using a wearable device. For example, the generation AI can display the evaluation results on a smartwatch, allowing the user to check the information at any time. This allows the user to receive the results of the convenience evaluation in real time.

[0066] The display unit can use the emotion estimation function to analyze the user's emotional response to the evaluation results and propose a display method that elicits positive emotions. The display unit can, for example, use the generation AI to analyze the user's emotional response to the evaluation results and propose a display method that elicits positive emotions. For example, it can highlight evaluation results that have positive emotions. The display unit can also use the emotion estimation function to monitor the user's emotions in real time and propose a display method that elicits positive emotions. For example, the generation AI can analyze the user's facial expressions and voice and highlight evaluation results that have positive emotions. This allows the user to preferentially view evaluation results that have positive emotions.

[0067] The display unit can compare the results of the convenience evaluation with the ratings and reviews of other users and provide a relative evaluation. The display unit, for example, uses a generation AI to compare the results of the convenience evaluation with the ratings and reviews of other users and provide a relative evaluation. For example, the display unit displays the evaluation results in comparison with other properties in the same area. The display unit can also provide a relative evaluation of the property based on the ratings and reviews of other users. For example, the generation AI analyzes the ratings of other users and displays the relative evaluation of the property. This allows the user to compare the ratings of other users and select a property.

[0068] The display unit can provide a function that links the results of the usability evaluation with the user's social media account and allows the results to be shared with friends and family. For example, the display unit can use the generation AI to link the results of the usability evaluation with the user's social media account and provide a function that allows the results to be shared with friends and family. For example, a function to post the evaluation results to a social media site can be added. The display unit can also share the evaluation results in real time through social media. For example, the generation AI posts the evaluation results on Facebook or Twitter to share them with friends and family. This allows the user to easily share the evaluation results with friends and family.

[0069] The display unit can use the emotion estimation function to collect other users' emotional reactions to the evaluation results and provide them as reference information. The display unit can use, for example, the generation AI to collect other users' emotional reactions and provide them as reference information. For example, it can prioritize displaying evaluation results with positive emotions. The display unit can also use the emotion estimation function to monitor other users' emotions in real time and provide them as reference information. For example, the generation AI can analyze other users' facial expressions and voices and prioritize displaying evaluation results with positive emotions. This allows users to refer to the emotional reactions of other users.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The real estate property selection system can further include an energy efficiency evaluation unit. The energy efficiency evaluation unit collects energy consumption data of properties and evaluates their energy efficiency. For example, the energy efficiency evaluation unit analyzes the property's electricity consumption and gas usage and scores its energy efficiency. The energy efficiency evaluation unit can also evaluate the property's insulation performance and whether or not it has a solar power generation system, and make suggestions for improving its energy efficiency. This makes it easier for users to select properties with high energy efficiency.

[0072] The real estate property selection system can further include a health assessment unit. The health assessment unit collects information on the air quality and noise levels around the property and evaluates the impact on health. For example, the health assessment unit monitors PM2.5 and CO2 concentrations around the property to evaluate air quality. The health assessment unit can also measure the noise levels around the property and suggest properties that offer a quiet environment. This makes it easier for users to select properties that are considerate of their health.

[0073] The real estate property selection system can further include an education evaluation unit. The education evaluation unit collects information on schools and educational facilities around the property and evaluates the educational environment. For example, the education evaluation unit collects academic achievement test results and advancement rates of schools around the property and scores the educational environment. The education evaluation unit can also collect information on cram schools and extracurricular activities around the property and make suggestions to improve the educational environment. This makes it easier for users to select properties suitable for their children's education.

[0074] The real estate property selection system can further use an emotion estimation function to estimate the user's stress level and suggest properties that will reduce stress. For example, the emotion estimation function can analyze the user's facial expressions and voice to evaluate the user's stress level. The emotion estimation function can also suggest properties with abundant natural environments or quiet environments to reduce the user's stress. This makes it easier for the user to select a property that will reduce stress.

[0075] The real estate property selection system can further use an emotion estimation function to estimate the user's happiness level and suggest properties that will improve the happiness level. For example, the emotion estimation function can analyze the user's facial expressions and voice to evaluate the happiness level. The emotion estimation function can also suggest properties with many green spaces and parks or properties with active community activities in order to improve the user's happiness level. This makes it easier for the user to select a property that will improve their happiness level.

[0076] The real estate property selection system can further use an emotion estimation function to estimate the user's level of relaxation and suggest properties that will help them relax. For example, the emotion estimation function can analyze the user's facial expressions and voice to evaluate their level of relaxation. The emotion estimation function can also suggest properties that are close to hot springs or spas or that offer a quiet environment so that the user can relax. This makes it easier for the user to select a property that will help them relax.

[0077] The real estate property selection system can further use an emotion estimation function to estimate the user's level of excitement and suggest properties that will elicit that excitement. For example, the emotion estimation function can analyze the user's facial expressions and voice to evaluate the level of excitement. The emotion estimation function can also suggest properties that are close to entertainment facilities or in areas with many events, in order to make the user feel excited. This makes it easier for the user to select properties that will elicit excitement.

[0078] The real estate property selection system can further use an emotion estimation function to estimate the user's sense of security and suggest properties that provide that sense of security. For example, the emotion estimation function can analyze the user's facial expressions and voice to evaluate the sense of security. The emotion estimation function can also suggest properties in areas with good public safety or with solid security measures to help the user feel a sense of security. This makes it easier for the user to select properties that provide a sense of security.

[0079] The real estate property selection system can further include an ecological evaluation unit. The ecological evaluation unit evaluates the environmental impact of a property and suggests ecologically friendly properties. For example, the ecological evaluation unit analyzes the property's energy consumption and recycling rate to score the environmental impact. The ecological evaluation unit can also collect information on ecological facilities and nature reserves around the property and suggest ecologically friendly properties. This makes it easier for users to select ecologically friendly properties.

[0080] The real estate property selection system can further include a transportation evaluation unit. The transportation evaluation unit evaluates transportation access around the property and suggests properties with high transportation convenience. For example, the transportation evaluation unit collects information on the operation status of public transportation and traffic congestion around the property and scores transportation convenience. The transportation evaluation unit can also collect information on bicycle paths and pedestrian-only roads around the property and make suggestions to improve transportation convenience. This makes it easier for users to select properties with high transportation convenience.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The surrounding facility information collection unit automatically collects information on facilities, transportation, and the natural environment around the real estate property. For example, using generation AI, information on supermarkets, convenience stores, hospitals, schools, stations, parks, etc. is collected based on the property's address and location information, and then mapped on a map. It is also possible to obtain the latest facility information using online databases and APIs. Step 2: The convenience evaluation unit evaluates the convenience of the surrounding area of ​​each property based on the user's preferences and priorities, using the information collected by the surrounding facility information collection unit. For example, the convenience of each property is scored based on items entered by the user, such as "distance from the station," "proximity to a supermarket," and "whether or not there is a park." It is also possible to use generation AI to analyze the user's preferences and priorities and set optimal evaluation criteria. Step 3: The display unit visually displays the results of the evaluation by the convenience evaluation unit. For example, it may display the locations of facilities around each property on a map, and indicate the distance and travel time to each facility. It may also display an overall score for each property based on the user's preferences and priorities.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a surrounding facility information collection unit that automatically collects information on facilities, transportation, natural environment, etc. that exist around the real estate property; a convenience evaluation unit that evaluates the convenience of the surrounding area of ​​each property based on the user's preferences and priorities, based on the information collected by the surrounding facility information collection unit; a display unit that visually displays the results of the evaluation by the usability evaluation unit. A system characterized by:

2. The surrounding facility information collection unit Collecting information on the opening hours and congestion status of the facility in real time to optimize the timing of users' visits 2. The system of claim 1.

3. The surrounding facility information collection unit Collecting information about events in the area and special seasonal activities, and making suggestions to the user 2. The system of claim 1.

4. The usability evaluation unit Based on the user's past search history and selection history, the generation AI automatically estimates the user's preferences and priorities and makes suggestions.

2. The system of claim 1.

5. The display unit The results of the usability evaluation are visually displayed on an interactive map or the 3D model, allowing the user to intuitively understand the results.

2. The system of claim 1.

6. The surrounding facility information collection unit Analyzes the user's feelings about nearby facilities and prioritizes displaying facilities with positive feelings 2. The system of claim 1.

7. The usability evaluation unit Analyze the user's feelings regarding the preferences and important points entered by the user and make suggestions that elicit positive feelings 2. The system of claim 1.

8. The display unit We analyze the user's emotional response to the evaluation results and propose a display method that elicits positive emotions.

2. The system of claim 1.

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